Random forest methods belong to the class of non-parametric machine learning algorithms. They were first introduced in 2001 by Breiman and they perform with accuracy in high dimensional settings. In this article, we consider, a simplified kernel-based random forest algorithm called simplified directional KeRF (Kernel Random Forest). We establish the asymptotic equivalence between simplified directional KeRF and centered KeRF, with additional numerical experiments supporting our theoretical results.

翻译：随机森林方法属于非参数机器学习算法类别，由Breiman于2001年首次提出，在高维环境中表现出良好的预测精度。本文研究了一种简化的基于核的随机森林算法，称为简化定向KeRF（核随机森林）。我们建立了简化定向KeRF与中心化KeRF之间的渐近等价性，并通过数值实验验证了理论结果。